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    Models of Parkinson's Disease Patient Gait

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    JBHI_PD_2019.pdf
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    Author
    Hughes, James Alexander
    Houghten, Sheridan
    Brown, Joseph Alexander
    Keyword
    Health Information Management
    Electrical and Electronic Engineering
    Computer Science Applications
    Biotechnology
    Parkinson's Disease
    Journal title
    IEEE Journal of Biomedical and Health Informatics
    Publication Volume
    24
    Publication Issue
    11
    Publication Begin page
    3103
    Publication End page
    3110
    
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10464/16887
    Abstract
    Parkinson's Disease is a disorder with diagnostic symptoms that include a change to a walking gait. The disease is problematic to diagnose. An objective method of monitoring the gait of a patient is required to ensure the effectiveness of diagnosis and treatments. We examine the suitability of Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) Models compared to Symbolic Regression (SR) using genetic programming that was demonstrated to be successful in previous works on gait. The XGBoost and ANN models are found to out-perform SR, but the SR model is more human explainable.
    ae974a485f413a2113503eed53cd6c53
    10.1109/jbhi.2019.2961808
    Scopus Count
    Collections
    Computer Science

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